BackgroundThe rise in prevalence of atopic dermatitis has been correlated with numerous elements of the exposome, modern-day lifestyle, and familial history. The combined analysis of familial history and other risk elements may allow us to understand the driving factors behind the development of atopic dermatitis. ObjectiveWe aimed to develop prediction models to assess the risk of developing atopic dermatitis using a large and diverse cohort (N=77,525) and easily-assessed risk factors. MethodsWe analyzed electronic medical record data from Leumit Health System. Documented predictive factors include sex, season of birth, environment (urban/rural), socio-economic status, household smoking, diagnosed skin conditions, number of siblings, a paternal, maternal or sibling history of an atopic condition, and antibiotic prescriptions during pregnancy or following birth. Predictive models were trained and validated on the dataset. ResultsMedium (OR 2.04, CI 1.92-2.17, p<0.001) and high (OR 2.13, CI 1.95-2.34, p<0.001) socioeconomic status, a previous diagnosis of contact dermatitis (OR 2.57, CI 2.37-2.78, p<0.001), presence of siblings with an AD diagnosis (OR 2.21, CI 2.04-2.40, p<0.001) and the percentage of siblings with any atopic condition (OR 2.58, CI 2.09-3.17, p<0.001) drove risk for AD in a logistic regression model. A random forest prediction model with a sensitivity of 61% and a specificity of 84% was developed. Generalized mixed models accounting for the random effect of familial relationships boasted an area under the curve of 0.98. ConclusionPredictive modeling using non-invasive and accessible inputs is a powerful tool to stratify risk for developing atopic dermatitis.